The agrarian crisis in India is one of the most pressing issues in the country, affecting hundreds of millions of people in their livelihoods, many of them small farmers and their families. The agrarian sector is in urgent need of reform and the issue has taken centre stage in public debate. This project brings together regional experts to better understand the geographical differences in agrarian distress. Working groups focus on regional agricultural issues and socio-economic risks for farmers, and make suggestions for region-specific policy solutions. Results inform complex decision-making processes surrounding agrarian reforms and contribute to finding long-term structural solutions.

In global south cities, population statistics about poor neighbourhoods are often unavailable or ignore large proportions of poor inhabitants. However, such statistics are urgently needed to support slum improvement, disaster response and health interventions. This research utilizes satellite images, machine learning and local data to estimate the number of poor inhabitants.

SPACE4ALL aims to unravel the climate vulnerability of slum communities in six larger and secondary cities by combining Citizen Science and Earth Observation methods. By combining Earth Observation data with qualitative, rich and diverse data from citizen science, local vulnerabilities can be captured. Thereupon, the data can train state-of-the-art Artificial Intelligence models. Normally these fall short because of insufficient data in such areas. The open-access results, which will be made freely available for local communities, will allow prioritizing risk hotspots in support of local information needs and measures.

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Climate change impacts are emerging as key drivers of forced migration, particularly among hundreds of millions of agrarian households in the developing world. There are signs that temporary male labour migration appears to offer an alternative way out for “trapped” agrarian populations, but little is otherwise known about this escalating trend. Forecasting climate migration is crucial to prepare for its major societal impacts, but existing approaches are coarse and datasets on labour migration almost non-existent. The novelty of this project is that I address these dire data gaps, and will forecast climate migration at very fine spatial scales. India is used as a case study. I combine advanced satellite-based weather observations with a systematic tracking of agrarian workforces across 250,000 settlements using village-level microdata. I investigate spatial correlations between historical climate change and movement out of farming. This quantitative macro-analysis “from above” is combined with primary data collection on labour migration “from below”, creating a unique, custom-made dataset that can be used for fine-scale climate migration modelling. This approach can serve as a new paradigm in this field of studies, and results can inform policy-makers on the ‘when’, ‘where’, and scale of future climate migration flows under different climate scenarios.